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analysis and prediction of air quality based on mobile crowd perception based on machine learning 24

Analysis and prediction of air quality based on mobile crowd...

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4th international conference on Control and Intelligent robotics, ICCIR 2024

作者： Feng, Yuechun Ningxia Institute of Technology Shizuishan753000 China

ISBN: (纸本)9798400709937

Air quality issues have attracted more and more public attention, and mobile crowd sensing uses the sensors of mobile devices such as smartphones to collect data, and through the participation of a large number of users, it can realize real-Time monitoring of the urban environment with multi-dimensional and high spatiotemporal resolution. In this paper, a variety of machine learning models are used to process and analyze mobile crowd perception data. Data preprocessing, feature engineering is used to extract air quality-related features, and machine learning algorithms are used for training and testing. Experimental results show that mobile crowd sensing based on machine learning is an effective air quality analysis and prediction model, which can not only supplement the existing air quality monitoring network, but also provide more personalized and real-Time air quality information for the public and decision-makers. © 2024 held by the owner/author(s).

关键词： Adversarial machine learning

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Reservoir Computing in Rehabilitation Video Analyses 10

Reservoir Computing in Rehabilitation Video Analyses

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10th IEEE international conference on Cybernetics and Intelligent Systems and IEEE conference on robotics, Automation and Mechatronics, CIS-RAM 2023

作者： Yang, Dongping Xu, Tie Lin, Feng Zhejiang Lab Research Institute for Artificial Intelligence Hangzhou China

ISBN: (纸本)9798350303087

Spatio-temporal information processing is fundamental in rehabilitation video analyses. Current strategies for spatio-temporal pattern recognition usually involve explicit feature extraction followed by feature aggregation, which requires a large amount of labeled data. There are two major challenges for recognizing complex spatio-temporal patterns. One is on extracting the representative features;and the other is on extracting the temporal structure, in particular, the temporal order, of image sequences. Reservoir Computing (RC) offers a promising tool by enabling automatic and accurate analyses of complex movement patterns in rehabilitation videos. Unlike traditional machine learning techniques, RC does not require explicit training of the reservoir connections, which is less data- demanding, more resistant to overfilling and easier to implement. The unique properties make it an attractive option for analyzing rehabilitation video data, where patients may experience changes in their movement patterns over time with a large data flow for processing. By analyzing new video data, the reservoir can adapt and refine its output to reflect the patient's current conditions. © 2023 IEEE.

关键词： Machine learning

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Research on the Application of Big Data analysis in Consumer Behavior pattern Recognition 24

Research on the Application of Big Data Analysis in Consumer...

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2024 international conference on Decision Science and Management, ICDSM 2024

作者： Wang, Zhenhua Shanghai University Shanghai200444 China

ISBN: (纸本)9798400718151

With the development and application of big data technology, the analysis of consumer behavior has become more in-depth and accurate. This research focuses on the application of big data analysis in consumer behavior pattern recognition, using a variety of data collection methods and analysis techniques, including principal component analysis (PCA), multiple linear regression analysis and association rule learning, aiming to explore consumer The complex relationship between age and gender on annual expenditures. Through empirical research, this article not only reveals the multidimensionality of consumer behavior and the impact of gender differences, but also confirms the effectiveness of big data analysis in revealing consumer behavior patterns. In addition, the study discusses the limitations of existing work and suggests possible directions for future research, including expanding data sources, in-depth exploration of the impact of sociocultural factors, and applying emerging technologies to improve research efficiency and accuracy. The results of this study not only provide valuable market insights for enterprises, but also provide new perspectives for future applications of consumer behavior research and big data analysis. © 2024 Copyright held by the owner/author(s).

关键词： Data accuracy

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33rd international conference on robotics in Alpe-Adria-Danube Region, RAAD 2024

33rd International Conference on Robotics in Alpe-Adria-Danu...

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33rd international conference on robotics in Alpe-Adria-Danube Region, RAAD 2024

ISBN: (纸本)9783031592560

The proceedings contain 64 papers. The special focus in this conference is on robotics in Alpe-Adria Danube Region. The topics include: Outliers—Do Image and Feature Domain Outliers Coincide in Robotic Applications?;an Algorithm for Determining the Coordinates of a Test Tube by a Robotic Aliquoting System Based on a Combination of the Hough Method and a Result Filtering Algorithm;deployment of Algorithms for Autonomous Drone Racing in a Real Environment;contactless Assembly of a Pi and a Plug Micropart with Electrostatic Force Fields;Modern Quality Control: Integrating Computer Vision in Inspection of PCB Elements;cost-Effective Robot Arm Teleoperation Via Human Pose Tracking with Monocular Camera;revolutionizing Speech Emotion Recognition: A Novel Hilbert Curve Approach for Two-Dimensional Representation and Convolutional Neural Network Classification;positioning of a Surgical Parallel Robot Using Artificial intelligence;effects of Increased Entropy on Robustness of Reinforcement Learning for Robot Box-Pushing;dynamic analysis of an Exoskeleton Robotic System for Stair Walking Assistance;a Compact Low-Frequencies Vibrational Bioreactor to Induce Cellular Response;design of Wearable Prostheses: A New Approach;workspace analysis for Laparoscopic Rectal Surgery: A Preliminary Study;an analysis of Virtual Reality Applications in Rehabilitation Engineering;low-Cost Functional Infrared Spectroscopy Based System as a Brain-Computer Interface;phenomenological Modelling of the Nonlinear Flexion–Extension Movement of Human Lower Limb Joints;continuous-Time Robust Control for Cancer Treatment Robots;accuracy and Repeatability of a Parallel Robot for Personalised Minimally Invasive Surgery;a Concise Review of Upper Limb Prostheses;analysis Study of Working Modes Within a Redundant Architecture for a Spherical Parallel Manipulator (SPM);MEDROVER: Medical Assistant Robot for Patient Monitoring and Treatment Management.

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2nd international conference on Explainable Artificial intelligence in the Digital Sustainability, AIRDS 2024

2nd International Conference on Explainable Artificial Intel...

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2nd international conference on Explainable Artificial intelligence in the Digital Sustainability, AIRDS 2024

ISBN: (纸本)9783031637162

The proceedings contain 25 papers. The special focus in this conference is on Explainable Artificial intelligence in the Digital Sustainability. The topics include: Advancing Sustainable Learning by Boosting Student Self-regulated Learning and Feedback Through AI-Driven Personalized in EFL Education;enabling Sustainable Learning Through Virtual robotics Machine Mediation of Social Interactions Between Teachers, Students, and Machines Based on Sociology Lens;the Role of Artificial intelligence in Improving Customer Service and Retaining Human Resources: Digital Sustainability as a Mediating Variable;Modelling Intelligent Agriculture Decision Support Tools to Boost Sustainable Digitalization: Evidence from MCDM Methods;"The Role of AI Applications in the Advertising and Design in Social Media Marketing: A Stride in the Direction of Marketing Sustainability";artificial intelligence and Environmental, Social and Governance: A Bibliometric analysis Review;the Mediating Influence of Energy Reduction on the Relationship Between Green Production and Digital Sustainability: Insights from Iraqi Oil Companies;exploring University Faculty Members’ Sustainable Innovative Behavior of Work with Artificial intelligence: A Review of the Literature;Exploring Customer Engagement Intentions with Interactive Smart Tables of AI for Full-Service Restaurants Sustainability;the Role of Artificial intelligence Trading Robots in Rationalizing Cryptocurrency Trading Decisions by Application to Bitcoin Currency;financing and Investing in Artificial intelligence: The Lucrative Benefits in Terms of Sustainable Digitalization;sustaining an Agile Supply Chain by Adopting Industry Technologies (4.0);the Investment of Human Capital in Digital Green Economy Transition to Support Artificial intelligence Technologies at Private Hospitals;Interpretive Structural Modeling (ISM) as an Artificial intelligence System for Improving Sustainable Performance.

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Current Approaches and Challenges in Medical Image analysis and Visually Explainable Artificial intelligence as Future Opportunities 5th

Current Approaches and Challenges in Medical Image Analysi...

引用

5th international conference on Deep Learning, Artificial intelligence and robotics, ICDLAIR 2023

作者： Sharma, Nandani Chaurasia, Sandeep Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur303007 India

ISBN: (纸本)9783031609343

Medical imaging plays a significant role in the disease diagnosis process. The automation techniques used today are helping medical professionals. The present methods’ Black-box structure makes it difficult for them to be implemented in the medical field with reliability. As a solution of the black box nature, an option is to use the XAI method. In this review, a discussion is done on the available popular visual sources of medical diagnosis. Further, the discussion is extended to the current position of machine learning and deep learning in the medical image domain. The performance of deep learning and machine learning approaches in medical image analysis is covered in the paper that is being presented. There is also a thorough examination of the difficulties these methods present for processing medical images in the real-time implementation of artificial intelligence techniques. Present XAI approaches as potential remedies for problems with deep learning approaches. In the paper discussion is on the different techniques that are available for the explanation of the outcome of the artificial intelligence models. In the last the future of the XAI in the medical domain are discussed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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An Overview of Artificial intelligence in the Detection of Suspicious Arabic Text on Social Media Platforms

An Overview of Artificial Intelligence in the Detection of S...

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2023 international conference on Artificial intelligence robotics, Signal and Image Processing, AIRoSIP 2023

作者： Albalawi, Amal Yafooz, Wael M.S. University of Prince Mugrin Department of Computer Science College of Computer and Cyber Sciences Madinah Saudi Arabia Taibah University Department of Computer Science College of Computer Science and Engineering Madinah Saudi Arabia

ISBN: (纸本)9798350327472

This study examines the challenges of detecting and classifying offensive language in Arabic, with a focus on natural language processing (NLP) methods. Arabic's distinctive linguistic characteristics, such as its extensive morphology, present substantial obstacles to accurately classifying offensive content. This paper evaluates the approaches currently in use in this field and conducts a critical analysis of publicly accessible datasets frequently used to detect offensive language in Arabic. In addition to highlighting areas for further study and growth in the detection of Arabic offensive language, the aim is to offer insights into the state-of-the-art methodologies being used. In the analysis of public datasets, this paper evaluates their suitability for offensive language detection tasks in Arabic, considering factors such as dataset size, annotation quality, and representation of diverse offensive language categories. Furthermore, this paper investigates the existing methods utilized for detecting and classifying offensive language in Arabic, examining a range of NLP approaches used in the context of detecting Arabic offensive language. This includes machine learning algorithms, deep learning architectures, and linguistic aspects, all tailored to address the specific challenges associated with offensive language detection in the Arabic vernacular. Our investigation emphasizes the need for customized algorithms that take into account Arabic's unique linguistic characteristics. Future research should focus on developing advanced models that incorporate domain-specific knowledge, handle dialectal variations, and effectively capture the intricacies of Arabic morphology. © 2023 IEEE.

关键词： Tweets

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Transforming Energy Efficiency in IoMT: Advanced Modulation Strategies for Sustainable Wireless Medical Sensor Networks 5

Transforming Energy Efficiency in IoMT: Advanced Modulation ...

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5th international conference on Data intelligence and Cognitive Informatics, ICDICI 2024

作者： Sadek, Ayoub Hadj Rattal, Salma Varshney, Gunjan Ar-Reyouchi, El Miloud Ghoumid, Kamal Mohammed I University Ensao Dept. of Electronics Informatics and Telecommunications Oujda Morocco Casablanca Morocco Jss University Noida Dept. of Robotics and Artificial Intelligence Noida India Abdelmalek Essaâdi University Dept. of Telecommunication and Computer Science Tetouan Morocco

ISBN: (纸本)9798350389609

The investigation focuses on advanced modulation strategies to optimize energy efficiency in Wireless Medical Sensor Networks (WMSNs) within the Internet of Medical Things (IoMT). The study addresses the balance between energy consumption and spectral efficiency, specifically targeting challenges associated with WMSN deployment in critical healthcare applications. The analysis rigorously evaluates energy consumption across various distances and modulation schemes, particularly in the presence of Additive White Gaussian Noise (AWGN) channels. Modulation techniques are categorized into linear and nonlinear types, allowing for a comprehensive examination of each class's energy efficiency. The objective is to minimize energy usage while ensuring reliable and secure communication in remote healthcare systems through the optimal selection of modulation schemes and transmission parameters. The results offer significant contributions to sustainable healthcare technology, enhancing the operational lifespan of medical sensors without compromising data transmission quality. © 2024 IEEE.

关键词： Gaussian noise (electronic)

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A Hybrid Elasto-Geometrical Calibration Method for Industrial Robot Using Only Position Measurement 17th

A Hybrid Elasto-Geometrical Calibration Method for Industria...

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17th international conference on Intelligent robotics and Applications

作者： Zhang, Zhongkai Lu, Yan Hu, Hongbo Shen, Zhikai Zhuang, Chungang Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9789819607822;9789819607839

In scenarios of manufacturing and high-precision operations, achieving high positioning accuracy of industrial robots is crucial. Kinematic calibration plays a vital role in reducing positioning errors. Considering both the geometric errors introduced by the machining and assembly processes and the joint compliance errors stemming from gearboxes, this paper establishes an elasto-geometrical error model based on the robot MDH model and the joint compliance model, and only position errors are required for calibration. The established model enables the identification of kinematic parameters, transformation frame parameters, and all joint stiffness under external loads. Furthermore, a hybrid calibration algorithm is proposed by combining least squares and heuristic algorithms, and redundancy is then analyzed and eliminated to further improve accuracy. By designing the hybrid algorithm, the proposed method can converge to the optimal solution quickly while maintaining sufficient global search capabilities. Finally, a simulation is conducted and the absolute positioning error decreases from 2.8658 mm to 0.1682 mm, validating the effectiveness of the proposed model and algorithm.

关键词： Elasto-geometrical calibration Joint stiffness Redundancy analysis Heuristic algorithm

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A Fuzzy Driven Framework for the Cognitive Modeling and Quantitative analysis of Aggregated Avatars 22

A Fuzzy Driven Framework for the Cognitive Modeling and Quan...

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IEEE Joint 22nd international Symposium on Computational intelligence and Informatics / 8th IEEE international conference on Recent Achievements in Mechatronics, Automation, Computer Science and robotics (CINTI-MACRo)

作者： Persa, Gyorgy Csapo, Adam B. Szechenyi Istvan Univ Doctoral Sch Multidisciplinary Engn Sci Gyor Hungary Obuda Univ Univ Res & Innovat Ctr Budapest Hungary

ISBN: (纸本)9798350398823

In this paper, our goal is to motivate the structured design and quantitative analysis of a class of avatars which we refer to as (fuzzy) aggregated avatars. Following a brief overview of the different avatar types based on the literature, we formulate a newly proposed definition of aggregated avatars, and also extend this concept with the more specialized class of fuzzy aggregated avatars. Further, we propose a framework to facilitate the design and experimental analysis of fuzzy aggregated avatars, and describe a use-case example in which user interaction patterns within a 3D virtual reality space are mapped onto dynamic avatar behaviors.

关键词： virtual reality Avatar design fuzzy aggregated avatars

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